Abstract
In recent scientific work, a classification-based approach to the machinery prognostics problem has been elaborated as an alternative to the Remaining Useful Life approaches. The classification-based approaches rely on a prediction horizon parameter, to which the model quality is sensitive. However, existing studies do not provide any means of determining this critical parameter. Instead, they rely on assumptions. We argue that the prediction horizon should be learned from data in order to overcome the challenges of its uncertainty. We propose a heuristic algorithm to learn the prediction horizon from data, as the first of its kind in the literature. We test its effectiveness with an ablation study based on a rich set of data. The results indicate a statistically significant improvement in model quality. This in turn increases the usability and generalizability of classification-based failure prediction approaches in the industry.
Supported by SAP SE, Walldorf, Germany.
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Baǧdelen, C., Paulheim, H., Döhring, M., Tauschinsky, A.F. (2022). Towards Generalizable Machinery Prognostics. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_21
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